With the increasing demand for electric vehicles, the interest in battery systems is growing. In order to enable safe operation of these complex energy storage systems, methods of fault diagnosis are needed. Particularly reconfigurable battery systems (RBS) with switches are promising on the way to fault tolerance as they allow the system to be reconfigured in the event of a fault. In this work, a model-based fault diagnosis algorithm is developed and validated that uses the switches of a RBS to improve the fault isolability. Since the algorithm changes the structure of the system in order to differentiate between non-isolable faults, it is classified as an active fault diagnosis algorithm. The deviations between sensor measurements and model, called residuals, are stochastically analyzed. For fault isolation a fuzzy clustering approach is used. A constrained sigma-point Kalman filter minimizes model uncertainties and therefore increases the sensitivity and robustness of the fault diagnosis approach. Furthermore, the filter allows estimating the fault amplitude in case of a fault. Based on active sequential hypothesis testing, a policy to calculate the next switch position is proposed and investigated. It is shown simulatively and experimentally that additional faults are isolated by the presented active approach

This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

Fault diagnosis is a central task of Battery Management Systems (BMS) of electric vehicle batteries. The effective implementation of fault diagnosis in the BMS can prevent costly and catastrophic consequences such as thermal runaway of battery cells. As fire incidents of electric vehicles show, the early detection of faults in the latent phase before a thermal runaway is still a problem. The goal is therefore to develop methods with high sensitivity and robustness that detect abnormalities in the battery system even under dynamic load profiles and sensor noise. This work presents a novel data-driven approach to fault diagnosis based on a comparison of single cell voltages. Faults are detected and localized by a statistical evaluation based on a Principal Component Analysis (PCA) of the data. To increase the sensitivity and robustness of the Cross-Cell Monitoring (CCM) method, an outlier robust sample studentization and a new method for selecting the number of principle components is proposed. The CCM data model is recursively updated, to handle non-stationarities caused by cell parameter changes. An application to the data of a large battery system consisting of 432 Lithium-ion cells shows the fault detection and isolation capability. The ability to learn and generalize is shown by an artificial parameter change and cross-validation.

Conventional automotive battery systems consisting of a large number of battery cells pose a variety of challenges in terms of safety, reliability, lifetime, and energy efficiency. Reconfigurable battery systems (RBSs) are a promising solution to these issues of conventional battery systems. However, the large number of components in RBS also increases the fault probability. To meet this challenge on the way to fault tolerance, this article addresses fault isolation in an RBS, which comprises two switches per cell. Based on an electrothermal model, a structural analysis is performed and a sensor set with optimal fault isolation properties is found. Since the system consists of many equations, a novel algorithm is introduced to efficiently calculate minimal structurally overdetermined (MSO) subsystems for fault diagnosis. For each fault, the algorithm allows determining the MSO set that has the least number of equations. A complexity analysis of the algorithm reveals that the proposed algorithm is computationally significantly less expensive for systems with high redundancy, such as RBS, than existing algorithms that compute all MSO sets. Since the algorithm considers the switch states, it is suitable for active fault isolation through switches. The application to the RBS shows that the electrical equations are prioritized over the thermal equations due to the model uncertainties. A video file demonstrating the proposed graph-based algorithm with an example is attached to this article.